




'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class   __A :



   """simple docstring"""







   A_												=				BlenderbotConfig
   A_												=				{}
   A_												=				'gelu'

   def __init__(      self				, _lowerCamelCase				, _lowerCamelCase=1_3				, _lowerCamelCase=7				, _lowerCamelCase=True				, _lowerCamelCase=False				, _lowerCamelCase=9_9				, _lowerCamelCase=3_2				, _lowerCamelCase=2				, _lowerCamelCase=4				, _lowerCamelCase=3_7				, _lowerCamelCase=0.1				, _lowerCamelCase=0.1				, _lowerCamelCase=2_0				, _lowerCamelCase=2				, _lowerCamelCase=1				, _lowerCamelCase=0				, )->       List[str]:
      lowercase__       							=							parent
      lowercase__       							=							batch_size
      lowercase__       							=							seq_length
      lowercase__       							=							is_training
      lowercase__       							=							use_labels
      lowercase__       							=							vocab_size
      lowercase__       							=							hidden_size
      lowercase__       							=							num_hidden_layers
      lowercase__       							=							num_attention_heads
      lowercase__       							=							intermediate_size

      lowercase__       							=							hidden_dropout_prob
      lowercase__       							=							attention_probs_dropout_prob
      lowercase__       							=							max_position_embeddings
      lowercase__       							=							eos_token_id
      lowercase__       							=							pad_token_id
      lowercase__       							=							bos_token_id

   def snake_case_(      self						)->       Union[str, Any]:
      lowercase__       							=							ids_tensor([self.batch_size, self.seq_length - 1]				, self.vocab_size						)
      lowercase__       							=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)				, 1						)
      lowercase__       							=							tf.concat([input_ids, eos_tensor]				, axis=1						)

      lowercase__       							=							ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size						)

      lowercase__       							=							self.config_cls(
          vocab_size=self.vocab_size				, d_model=self.hidden_size				, encoder_layers=self.num_hidden_layers				, decoder_layers=self.num_hidden_layers				, encoder_attention_heads=self.num_attention_heads				, decoder_attention_heads=self.num_attention_heads				, encoder_ffn_dim=self.intermediate_size				, decoder_ffn_dim=self.intermediate_size				, dropout=self.hidden_dropout_prob				, attention_dropout=self.attention_probs_dropout_prob				, max_position_embeddings=self.max_position_embeddings				, eos_token_ids=[2]				, bos_token_id=self.bos_token_id				, pad_token_id=self.pad_token_id				, decoder_start_token_id=self.pad_token_id				, **self.config_updates				, )
      lowercase__       							=							prepare_blenderbot_inputs_dict(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase						)
      return config, inputs_dict

   def snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase						)->       Union[str, Any]:
      lowercase__       							=							TFBlenderbotModel(config=_lowerCamelCase						).get_decoder()
      lowercase__       							=							inputs_dict['''input_ids''']

      lowercase__       							=							input_ids[:1, :]
      lowercase__       							=							inputs_dict['''attention_mask'''][:1, :]
      lowercase__       							=							inputs_dict['''head_mask''']
      lowercase__       							=							1

      # first forward pass
      lowercase__       							=							model(_lowerCamelCase				, attention_mask=_lowerCamelCase				, head_mask=_lowerCamelCase				, use_cache=_lowerCamelCase						)

      lowercase__							,					lowercase__       							=							outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      lowercase__       							=							ids_tensor((self.batch_size, 3)				, config.vocab_size						)
      lowercase__       							=							tf.cast(ids_tensor((self.batch_size, 3)				, 2						)				, tf.inta						)

      # append to next input_ids and
      lowercase__       							=							tf.concat([input_ids, next_tokens]				, axis=-1						)
      lowercase__       							=							tf.concat([attention_mask, next_attn_mask]				, axis=-1						)

      lowercase__       							=							model(_lowerCamelCase				, attention_mask=_lowerCamelCase						)[0]
      lowercase__       							=							model(_lowerCamelCase				, attention_mask=_lowerCamelCase				, past_key_values=_lowerCamelCase						)[0]

      self.parent.assertEqual(next_tokens.shape[1]				, output_from_past.shape[1]						)

      # select random slice
      lowercase__       							=							int(ids_tensor((1,)				, output_from_past.shape[-1]						)						)
      lowercase__       							=							output_from_no_past[:, -3:, random_slice_idx]
      lowercase__       							=							output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(_lowerCamelCase				, _lowerCamelCase				, rtol=1e-3						)






def 		_lowerCAmelCase							(   lowercase							: List[Any]					,  lowercase							: Dict					,  lowercase							: str					,  lowercase							: List[str]=None					,  lowercase							: List[Any]=None					,  lowercase							: str=None					,  lowercase							: Tuple=None					,  lowercase							: List[Any]=None					,  )			->Tuple:
   """simple docstring"""




   if attention_mask is None:
      lowercase__       							=							tf.cast(tf.math.not_equal(a_					,  config.pad_token_id    )					,  tf.inta    )
   if decoder_attention_mask is None:
      lowercase__       							=							tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape					,  dtype=tf.inta    ),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,  config.pad_token_id    )					,  tf.inta    ),
          ]					,  axis=-1					,  )
   if head_mask is None:
      lowercase__       							=							tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
   if decoder_head_mask is None:
      lowercase__       							=							tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
   if cross_attn_head_mask is None:
      lowercase__       							=							tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }
@require_tf
class   __A (							a     ,   a     ,   unittest.TestCase  ):



   """simple docstring"""







   A_												=				(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
   A_												=				(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
   A_												=				(
       {
           'conversational': TFBlenderbotForConditionalGeneration,
           'feature-extraction': TFBlenderbotModel,
           'summarization': TFBlenderbotForConditionalGeneration,
           'text2text-generation': TFBlenderbotForConditionalGeneration,
           'translation': TFBlenderbotForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   A_												=				True
   A_												=				False
   A_												=				False

   def snake_case_(      self						)->       Optional[Any]:
      lowercase__       							=							TFBlenderbotModelTester(self						)
      lowercase__       							=							ConfigTester(self				, config_class=_lowerCamelCase						)

   def snake_case_(      self						)->       Optional[int]:
      self.config_tester.run_common_tests()

   def snake_case_(      self						)->       int:
      lowercase__       							=							self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase						)



@require_tokenizers
@require_tf
class   __A (							unittest.TestCase  ):



   """simple docstring"""







   A_												=				['My friends are cool but they eat too many carbs.']
   A_												=				'facebook/blenderbot-400M-distill'

   @cached_property
   def snake_case_(      self						)->       Any:
      return BlenderbotTokenizer.from_pretrained(self.model_name						)

   @cached_property
   def snake_case_(      self						)->       Tuple:
      lowercase__       							=							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
      return model

   @slow
   def snake_case_(      self						)->       int:
      lowercase__       							=							self.tokenizer(self.src_text				, return_tensors='''tf'''						)
      lowercase__       							=							self.model.generate(
          model_inputs.input_ids				, )
      lowercase__       							=							self.tokenizer.batch_decode(generated_ids.numpy()				, skip_special_tokens=_lowerCamelCase						)[0]
      assert (
          generated_words
          == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
      )










'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



